AI Decision-Support for Ops: Data to Audited Decisions
A guided, multi-course journey that builds this skill from the ground up. Work through the chapters in order, or jump to any lesson.
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Decision-Support Boundaries and Human Decision OwnershipRisk Tiers for Trust, Override, and EscalationInput and Output Contracts for AI-Assisted Ops ArtifactsDecision Logs That Capture Evidence, Critique, and OverridesOps Data Readiness and Ground Truth DefinitionsLightweight QA, Stop Rules, and Automation Creep Controls
Minimum Viable Demand History for Planning ForecastsNaive and Seasonal-Naive Baselines as Default ComparatorsRolling-Origin Backtests and Accuracy vs Bias MeasuresUncertainty Ranges and Planning Buffers From Prediction IntervalsEvidence-Cited Forecast Narratives and Driver HygieneOverride Decisions and Forecast Pack Sign-Off
Event Log Readiness for Flow AnalysisCycle Time Decomposition and Bottleneck SignalsVariant and Rework Loop Discovery With AI That Must Show EvidenceLittle’s Law as a Reality Check on Bottleneck NarrativesConformance Checks Against the SOP and Controlled Intervention SelectionAutomation as a Controlled Change With Exceptions and Monitoring
AI-Assisted SOP Drafting With Controls, Exceptions, and CitationsSOP Validation for Hallucinations, Control Gaps, and Change RiskDocument Control That Ties SOP Changes to Measured IssuesIncident Signals and Thresholds That Create Owners, Not NoiseRunbooks and Alert Tuning With Post-Incident Feedback LoopsSLA Scorecards, Evidence Packs, and the Integrated Control Loop